MRI and CT Image Annotation - V7 AI Academy
Science & Technology
Introduction
MRI and CT scans produce planar sliced data, which consists of a series of slices that represent different cross-sections of the body. For AI models to effectively identify objects within these images, it is crucial to embed relevant objects in each slice. In this article, we will explore how to carry out image segmentation for MRI and CT scans using the V7 Darwin platform.
Creating a Dataset
To get started, we first need to create a dataset. We will assign a name to our dataset and proceed to upload our data. This can be done either through the browser or using the Command Line Interface (CLI). To use the CLI, start by installing Darwin PI and utilize the command darwin dataset push <dataset_name> <path_to_files>. Detailed commands and shortcuts can be found on V7 documentation.
Once the data upload is complete, we can proceed to create classes for annotation. For this example, we will create a "heart" class and choose a polygon mask as our annotation type. We can also introduce subtypes such as directional vectors and instance IDs to provide a detailed classification of the objects.
In our dataset, we will examine three cases. The filtering options on the right will help us navigate through the data efficiently. Under the classes tab, we can view all created classes and their corresponding annotation types. The overview tab provides insights into the dataset's progress and the frequency of various classes.
Manual and Automatic Annotation
The V7 Darwin platform allows for manual labeling of images, similar to what you find in most image annotation tools or DICOM viewers. For instance, you can create polygon masks to annotate specific areas of the images. However, leveraging AI capabilities accelerates this process significantly. By using the auto-annotate feature (activated by the shortcut 'N'), Darwin intelligently segments objects, such as white matter lesions, by painting their pixels accurately.
You can refine the auto-generated annotations by including or excluding areas by clicking outside or inside the polygon, respectively, marked by red indicators. Additionally, comments can be added to images for collaboration purposes with colleagues or other members of the annotation team. Adjusting the image manipulation settings can help improve visibility, especially in faint medical images.
As we annotate these white matter lesions, Darwin continuously adapts to improve the accuracy based on our input. The entire segmentation process for this case can be completed in just a few minutes.
Segmenting Kidneys
Next, we will segment the kidneys of a healthy patient using similar strategies. After creating a few auto-annotations to exclude unnecessary markings, we can reveal the instance ID for each kidney. These instance IDs consist of unique color combinations that assist in tracking each organ throughout the slices.
While analyzing the subsequent slices, we can utilize the "copy instances" button to replicate previous annotations, adjusting any changes as needed. Keeping track of the annotations ensures consistent identification of each kidney. This entire process also takes just a few minutes.
Segmenting the Aorta
In our final case, we will segment the aorta, incorporating attributes to enhance the annotations. Attributes function similarly to tags, giving them additional descriptive power. Each slice segmentation takes approximately 10 to 20 seconds, gradually creating a volumetric map of the aorta.
As we continue annotating, Darwin adjusts to any shape alterations. When we notice an issue, such as an aneurysm, we can change the attribute accordingly. By tracking our progress and copying annotations as we move across slices, we can successfully document the aneurysm. After around five minutes, a complete segmentation of the aorta in 3D slices is achieved.
Conclusion
This guide demonstrates just one aspect of the capabilities offered by V7 Darwin for AI-powered image segmentation. The platform excels at adapting to various organs and identifying anomalies, paving the way for enhanced insights in medical imagery. For further exploration of such tools, visit V7 Labs.
Keywords
- MRI
- CT Scan
- Image Annotation
- V7 Darwin
- Dataset Creation
- Auto-annotation
- Polygon Mask
- Image Segmentation
- Instance ID
- Aneurysm
FAQ
Q1: What is the purpose of using AI in MRI and CT image annotation?
A1: AI helps automate the annotation process, improving efficiency and accuracy in identifying objects within medical image slices.
Q2: How can I create a dataset on V7 Darwin?
A2: You can create a dataset via the browser or CLI using the command darwin dataset push <dataset_name> <path_to_files>.
Q3: What is the significance of instance IDs in image segmentation?
A3: Instance IDs help track individual instances of organs or structures throughout various image slices, ensuring consistency in annotation.
Q4: How does the auto-annotate feature work?
A4: The auto-annotate feature employs a deep learning model to intelligently segment images, making initial annotations based on learned patterns.
Q5: Can I manually adjust annotations after the AI has generated them?
A5: Yes, you can manually refine annotations by including or excluding areas after auto-annotation and adjust as necessary.
